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Monitoring of engineered systems consists of characterizing the normal behavior of the system and tracking departures from it. Techniques to monitor a system can be split into two classes based on their use of inputs and outputs of the system. Systems-based monitoring refers to the case when both inputs and outputs of a system are available and utilized. Conversely, symptomatic monitoring refers to the case when only outputs of the system are available. This thesis extended symptomatic and systems-based monitoring of biomedical systems via the use of non-stationary signal processing and advanced monitoring methods. Monitoring of various systems of the human body is encumbered by several key hurdles. First, current biomedical knowledge may not fully comprehend the extent of inputs and outputs of a particular system. In addition, regardless of current knowledge, inputs may not be accessible and outputs may be, at best, indirect measurements of the underlying biological process. Finally, even if inputs and outputs are measurable, their relationship may be highly nonlinear and convoluted. These hurdles require the use of advanced signal processing and monitoring approaches. Regardless of the pursuit of symptomatic or system-based monitoring, the aforementioned hurdles can be partially overcome by using non-stationary signal analysis to reveal the way frequency content of biomedical signals change over time. Furthermore, the use of advanced classification and monitoring methods facilitated reliable differentiation between various conditions of the monitored system based on the information from non-stationary signal analysis. The human brain was targeted for advancement of symptomatic monitoring, as it is a system responding to a plethora internal and external stimuli. The complexity of the brain makes it unfeasible to realize system-based monitoring to utilize all the relevant inputs and outputs for the brain. Further, measurement of brain activity (outputs), in the indirect form of electroencephalogram (EEG), remains a workhorse of brain disorder diagnosis. In this thesis, advanced signal processing and pattern recognition methods are employed to devise and study an epilepsy detection and localization algorithm that outperforms those reported in literature. This thesis also extended systems-based monitoring of human biomedical systems via advanced input-output modeling and sophisticated monitoring techniques based on the information from non-stationary signal analysis. Explorations of system-based monitoring in the NMS system were driven by the fact that joint velocities and torques can be seen NMS responses to electrical inputs provided by the central nervous system (CNS) and the electromyograph (EMG) provides an indirect measurement of CNS excitations delivered to the muscles. Thus, both inputs and outputs of this system are more or less available and one can approach its monitoring via the use of system-based approaches. / text

Contact mechanics of rough surfaces and surface wear will be considered. Two types of failures are considered. The first involving rapidly growing failure and the second fatigue type surface failure as a result of repetitive application of load cycle. The first type of failure will be identified based on surface hysteresis energy loss in a load/unload cycle or examination of fracture toughness of the material near its rough surface. The above approach will be used to examine both types of failure in joint implants in the human body. These include consideration of implants for hip, ankle, spine and knee. In this case rapid and/or fatigue failures will be considered and related to anticipated implant life cycle based on implant recipient's tolerance level. Hence surface fidelity in terms of the biological host's tolerance of toxicity level due to wear will be used to develop life cycle prediction of an implant. The second application, rapid and fatigue wear will be examined in commonly used mechanical systems that include spur and helical gearing and rolling element bearings.

Novel techniques for high-performance wireless power transmission and data interfacing with implantable medical devices (IMDs) were proposed. Several system- and circuit-level techniques were developed towards the design of a novel wireless data and power transmission link for a multi-channel inductively-powered wireless implantable neural-recording and stimulation system. Such wireless data and power transmission techniques have promising prospects for use in IMDs such as biosensors and neural recording/stimulation devices, neural interfacing experiments in enriched environments, radio-frequency identification (RFID), smartcards, near-field communication (NFC), wireless sensors, and charging mobile devices and electric vehicles. The contributions in wireless power transfer are the development of an RFID-based closed-loop power transmission system, a high-performance 3-coil link with optimal design procedure, circuit-based theoretical foundation for magnetic-resonance-based power transmission using multiple coils, a figure-of-merit for designing high-performance inductive links, a low-power and adaptive power management and data transceiver ASIC to be used as a general-purpose power module for wireless electrophysiology experiments, and a Q-modulated inductive link for automatic load matching. In wireless data transfer, the contributions are the development of a new modulation technique called pulse-delay modulation for low-power and wideband near-field data communication and a pulse-width-modulation impulse-radio ultra-wideband transceiver for low-power and wideband far-field data transmission.

<p>Advances in additive manufacturing technologies enable the rapid, high-throughput generation of mechanically soft microelectromechanical devices with tailored designs for many applications spanning from optical to biomedical applications. These devices can be softly interfaced with biological tissues and mechanically fragile systems, which enables to open up a whole new range of applications. However, the scalable production of these devices faces a significant challenge due to the complexity of the microfabrication process and the intolerable thermal, chemical, and mechanical conditions of their flexible polymeric substrates. To overcome these limitations, I have developed a set of advanced additive manufacturing technologies enabling (1) mechanics-driven manufacturing of quasi-three-dimensional (quasi-3D) nanoarchitectures with arbitrary substrate materials and structures; (2) repetitive replication of quasi-3D nanoarchitectures for infrared (IR) bandpass filtering; (3) electrochemical reaction-driven delamination of thin-film electronics over wafer-scale; (4) rapid custom printing of soft poroelastic materials for biomedical applications. </p> <p>First, I have developed a new mechanics-driven nanomanufacturing method enabling large-scale production of quasi-3D plasmonic nanoarchitectures that are capable of controlling light at nanoscale length. This method aims to eliminate the need for repetitive uses of conventional nanolithography techniques that are time- and cost-consuming. This approach is innovative and impactful because, unlike any of the conventional manufacturing methods, the entire process requires no chemical, thermal, and mechanical treatments, enabling a large extension of types of receiver substrate to nearly arbitrary materials and structures. Pilot deterministic assembly of quasi-3D plasmonic nanoarrays with imaging sensors yields the most important advances, leading to improvements in a broad range of imaging systems. Comprehensive experimental and computational studies were performed to understand the underlying mechanism of this new manufacturing technique and thereby provide a generalizable technical guideline to the manufacturing society. The constituent quasi-3D nanoarchitectures achieved by this manufacturing technology can broaden considerations further downscaled plasmonic metamaterials suggest directions for future research.</p> <p>Second, I have developed mechanics-driven nanomanufacturing that provides the capability to repetitively replicate quasi-3D plasmonic nanoarchitectures even with the presence of an extremely brittle infrared-transparent spacer, such as SU-8, thereby manipulating IR light (e.g., selectively transmitting a portion of the IR spectrum while rejecting all other wavelengths). Comprehensive experimental and computational studies were performed to understand the underlying nanomanufacturing mechanism of quasi-3D plasmonic nanoarchitectures. The spectral features such as the shape of the transmission spectrum, peak transmission and full width at half maximum (FWHM), etc. were studied to demonstrate the bandpass filtering effect of the assembled quasi-3D plasmonic nanoarchitecture.</p> <p>Third, I have developed an electrochemical reaction-driven transfer printing method enabling a one-step debonding of large-scale thin-film devices. Conventional transfer printing methods have critical limitations associated with an efficient and intact separation process for flexible 3D plasmonic nanoarchitectures or bio-integrated electronics at a large scale. The one-step electrochemical reaction-driven method provides rapid delamination of large-scale quasi-3D plasmonic nanoarchitectures or bio-integrated electronics within a few minutes without any physical contact, enabling transfer onto the target substrate without any defects and damages. This manufacturing technology enables the rapid construction of quasi-3D plasmonic nanoarchitectures and bio-integrated electronics at a large scale, providing a new generation of numerous state-of-art optical and electronic systems.</p> <p>Lastly, I have developed a new printing method enabling the direct ink writing (DIW) of multidimensional functional materials in an arbitrary shape and size to rapidly prototype stretchable biosensors with tailored designs to meet the requirement of adapting the geometric nonlinearity of a specific biological site in the human body. Herein, we report a new class of a poroelastic silicone composite that is exceptionally soft and insensitive to mechanical strain without generating significant hysteresis, which yields a robust integration with living tissues, thereby enabling both a high-fidelity recording of spatiotemporal electrophysiological activity and real-time ultrasound imaging for visual feedback. Comprehensive <i>in vitro</i>, <i>ex vivo</i>, and <i>in vivo</i> studies provide not only to understand the structure-property-performance relationships of the biosensor but also to evaluate infarct features in a murine acute myocardial infarction model. These features show a potential clinical utility in the simultaneous intraoperative recording and imaging on the epicardial surface, which may guide a definitive surgical treatment.</p>

Ce travail s'inscrit dans le cadre d'une thèse CIFRE - Convention Industrielle de Formation - supervisée par la laboratoire LAMIH UMR CNRS 8201 Laboratoire et financé par l'entreprise SRDEP (Société de Ressources et de Développement pour les Entreprises et les Particuliers) et l'ANRT (Association Nationale de la Recherche et de la Technologie).Il s'agit d'une thèse applicative dans le domaine de la commande robuste numérique des systèmes biomécaniques. L'objectif est de concevoir et de réaliser un appareil de ré-entraînement à l'effort physique pour contrôler les paramètres physiologiques de l'homme avec plusieurs finalités : diagnostic, l'optimisation des prescriptions et sécurisation de l'exercice.Le travail réalisé de divise en deux parties :1 - L'étude scientifique : Dans cette partie, un modèle non linéaire de fréquence cardiaque est proposé ainsi qu'une méthodologie d'identification. A partir des résultats obtenus dans l'étape précédente, est développé un algorithme de commande robuste. Ce dernier est validé à partir de plusieurs tests réalisés en temps réel sur l'homme à l'aide d'un prototype.2 - Réalisation et industrialisation : Cela comprend la conception de l'appareil, le dimensionnement des différentes parties, la conception du système embarqué temps réel, la fabrication des cartes électroniques, l'implantation des algorithmes de commandes ainsi que la gestion des relations avec les partenaires et les sous-traitants.Il s'agit donc clairement d'un projet multidisciplinaire incluant de la mécanique, de l'électronique, de l'informatique, de l'automatique mais aussi le design, des relations clients-fournisseurs, des démarches de contacts avec des clients potentiels et de nombreux essais et mises au point. Dans cette thèse, seuls les aspects scientifiques seront discutés, en particulier ceux centrés autour des techniques de l'automatique : Modélisation, identification, commande et diagnostic même si l'ensemble des activités non liées à ces aspects ont pris un certain temps. / This work of PhD Thesis is realized in the framework of "CIFRE" scholarship supervised by LAMIH UMR CNRS 8201 laboratory and financed by SRDEP (Société de Ressources et de Développement pour les Entreprises et les Particuliers) Company and the ANRT (Association Nationale de la Recherche et de la Technologie) organization. It falls in the domain of numerical identification and robust control of biomedical systems. The aim is to design and to realize a prototype of physical training apparatus based on an electrically controlled brake to regulate some physiological parameters (namely heart rate) by means of exercise intensity. This might be used for diagnostic, optimization of exercise prescriptions and in making exercise more secure for patients.The work can be divided into two parts:1 - Scientific study : in this part, a nonlinear model is proposed for the heart rate/exercise intensity system as well as an identification process. Then, a robust control strategy is established and tested in real time on several subjects using thedeveloped prototype.2 - Realization of embedded system : This includes the design of electromechanical system; the real time embedded system, electronic cards as well as the implementation of control algorithms.It is clearly a multidisciplinary project including mechanics, electronics, informatics, control systems, physiology of human exercise besides project management, relations with partners, and direction of team work. In this thesis only scientific aspects will be discussed and especially those relevant to system identification and control, despite the fact that a relatively big percentage of time had to be devoted to the other aspects.

System identification finds nowadays application in various areas of biological research as a tool of empiric mathematical modeling and model individualization. A fundamental challenge of system identification in biology awaits in the form of response variability. Furthermore, biological systems tend to exhibit high degree of nonlinearity as well as significant time delays. This thesis covers system identification approaches developed for the applications within two particular biomedical fields: neuroscience and endocrinology. The first topic of the thesis is parameter estimation of the classical Elementary Motion Detector (EMD) model in insect vision. There are two important aspects to be taken care of in the identification approach, namely the nonlinear dynamics of the individual EMD and the spatially distributed structure of multiple detectors producing a measurable neural response. Hence, the suggested identification method is comprised of two consecutive stages addressing each of the above aspects. Furthermore, visual stimulus design for high spatial excitation order has been investigated. The second topic is parameter estimation of mathematical model for testosterone regulation in the human male. The main challenges of this application are in the unavailability of input signal measurements and the presence of an unknown pulsatile feedback in the system resulting in a highly nonlinear closed-loop dynamics. Semi-blind identification method has been developed based on a recently proposed pulse-modulated model of pulsatile endocrine regulation. The two system identification problems treated in the thesis bear some resemblance in the sense that both involve measured signals that can be seen as square-integrable functions of time. This property is handled by transforming the signals into the Laguerre domain, i.e. by equivalently representing the functions with their infinite Laguerre series.

For most of the naturally occurring signals, especially biomedical signals, the underlying physical process generating the signal is often not fully known, making it difficult to obtain a parametric model. Therefore, signal processing techniques are used to analyze the signal for non-parametrically characterizing the underlying system from which the signals are produced. Most of the real life systems are nonlinear and time varying, which poses a challenge while characterizing them. Additionally, multiple sensors are used to extract signals from such systems, resulting in multichannel signals which are inherently coupled. In this thesis, we counter this challenge by using Recurrence Plot based techniques for characterizing biomedical systems such as heart or brain, using signals such as heart rate variability (HRV), electroencephalogram(EEG) or functional magnetic resonance imaging (fMRI), respectively, extracted from them. In time series analysis, it is well known that a system can be represented by a trajectory in an N-dimensional state space, which completely represents an instance of the system behavior. Such a system characterization has been done using dynamical invariants such as correlation dimension, Lyapunov exponent etc. Takens has shown that when the state variables of the underlying system are not known, one can obtain a trajectory in ‘phase space’ using only the signals obtained from such a system. The phase space trajectory is topologically equivalent to the state space trajectory. This enables us to characterize the system behavior from only the signals sensed from them. However, estimation of correlation dimension, Lyapunov exponent, etc, are vulnerable to non-stationarities in the signal and require large number of sample points for accurate computation, both of which are important in the case of biomedical signals. Alternatively, a technique called Recurrence Plots (RP) has been proposed, which addresses these concerns, apart from providing additional insights. Measures to characterize RPs of single and two channel data are called Recurrence Quantification Analysis (RQA) and cross RQA (CRQA), respectively. These methods have been applied with a good measure of success in diverse areas. However, they have not been studied extensively in the context of experimental biomedical signals, especially multichannel data. In this thesis, the RP technique and its associated measures are briefly reviewed. Using the computational tools developed for this thesis, RP technique has been applied on select single channel, multichannel and multimodal (i.e. multiple channels derived from different modalities) biomedical signals. Connectivity analysis is demonstrated as post-processing of RP analysis on multichannel signals such as EEG and fMRI. Finally, a novel metric, based on the modification of a CRQA measure is proposed, which shows improved results. For the case of single channel signal, we have considered a large database of HRV signals of 112 subjects recorded for both normal and abnormal (anxiety disorder and depression disorder) subjects, in both supine and standing positions. Existing RQA measures, Recurrence Rate and Determinism, were used to distinguish between normal and abnormal subjects with an accuracy of 58.93%. A new measure, MLV has been introduced, using which a classification accuracy of 98.2% is obtained. Correlation between probabilities of recurrence (CPR) is a CRQA measure used to characterize phase synchronization between two signals. In this work, we demonstrate its utility with application to multimodal and multichannel biomedical signals. First, for the multimodal case, we have computed running CPR (rCPR), a modification proposed by us, which allows dynamic estimation of CPR as a function of time, on multimodal cardiac signals (electrocardiogram and arterial blood pressure) and demonstrated that the method can clearly detect abnormalities (premature ventricular contractions); this has potential applications in cardiac care such as assisted automated diagnosis. Second, for the multichannel case, we have used 16 channel EEG signals recorded under various physiological states such as (i) global epileptic seizure and pre-seizure and (ii) focal epilepsy. CPR was computed pair-wise between the channels and a CPR matrix of all pairs was formed. Contour plot of the CPR matrix was obtained to illustrate synchronization. Statistical analysis of CPR matrix for 16 subjects of global epilepsy showed clear differences between pre-seizure and seizure conditions, and a linear discriminant classifier was used in distinguishing between the two conditions with 100% accuracy. Connectivity analysis of multichannel EEG signals was performed by post-processing of the CPR matrix to understand global network-level characterization of the brain. Brain connectivity using thresholded CPR matrix of multichannel EEG signals showed clear differences in the number and pattern of connections in brain connectivity graph between epileptic seizure and pre-seizure. Corresponding brain headmaps provide meaningful insights about synchronization in the brain in those states. K-means clustering of connectivity parameters of CPR and linear correlation obtained from global epileptic seizure and pre-seizure showed significantly larger cluster centroid distances for CPR as opposed to linear correlation, thereby demonstrating the efficacy of CPR. The headmap in the case of focal epilepsy clearly enables us to identify the focus of the epilepsy which provides certain diagnostic value. Connectivity analysis on multichannel fMRI signals was performed using CPR matrix and graph theoretic analysis. Adjacency matrix was obtained from CPR matrices after thresholding it using statistical significance tests. Graph theoretic analysis based on communicability was performed to obtain community structures for awake resting and anesthetic sedation states. Concurrent behavioral data showed memory impairment due to anesthesia. Given the fact that previous studies have implicated the hippocampus in memory function, the CPR results showing the hippocampus within the community in awake state and out of it in anesthesia state, demonstrated the biological plausibility of the CPR results. On the other hand, results from linear correlation were less biologically plausible. In biological systems, highly synchronized and desynchronized systems are of interest rather than moderately synchronized ones. However, CPR is approximately a monotonic function of synchronization and hence can assume values which indicate moderate synchronization. In order to emphasize high synchronization/ desynchronization and de-emphasize moderate synchronization, a new method of Correlation Synchronization Convergence Time (CSCT) is proposed. It is obtained using an iterative procedure involving the evaluation of CPR for successive autocorrelations until CPR converges to a chosen threshold. CSCT was evaluated for 16 channel EEG data and corresponding contour plots and histograms were obtained, which shows better discrimination between synchronized and asynchronized states compared to the conventional CPR. This thesis has demonstrated the efficacy of RP technique and associated measures in characterizing various classes of biomedical signals. The results obtained are corroborated by well known physiological facts, and they provide physiologically meaningful insights into the functioning of the underlying biological systems, with potential diagnostic value in healthcare.